Spaces:
Build error
Build error
File size: 3,968 Bytes
45ee559 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
import os
# Trainer: Where the ✨️ happens.
# TrainingArgs: Defines the set of arguments of the Trainer.
from trainer import Trainer, TrainerArgs
# GlowTTSConfig: all model related values for training, validating and testing.
from TTS.tts.configs.glow_tts_config import GlowTTSConfig
# BaseDatasetConfig: defines name, formatter and path of the dataset.
from TTS.tts.configs.shared_configs import BaseAudioConfig, BaseDatasetConfig, CharactersConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.glow_tts import GlowTTS
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
# we use the same path as this script as our training folder.
output_path = "/storage/output-glowtts/"
# DEFINE DATASET CONFIG
# Set LJSpeech as our target dataset and define its path.
# You can also use a simple Dict to define the dataset and pass it to your custom formatter.
dataset_config = BaseDatasetConfig(
formatter="bel_tts_formatter",
meta_file_train="ipa_final_dataset.csv",
path=os.path.join(output_path, "/storage/filtered_dataset/"),
)
characters = CharactersConfig(
characters_class="TTS.tts.utils.text.characters.Graphemes",
pad="_",
eos="~",
bos="^",
blank="@",
characters="Iabdfgijklmnprstuvxzɔɛɣɨɫɱʂʐʲˈː̯͡β",
punctuations="!,.?: -‒–—…",
)
audio_config = BaseAudioConfig(
mel_fmin=50,
mel_fmax=8000,
hop_length=256,
stats_path="/storage/TTS/scale_stats.npy",
)
# INITIALIZE THE TRAINING CONFIGURATION
# Configure the model. Every config class inherits the BaseTTSConfig.
config = GlowTTSConfig(
batch_size=96,
eval_batch_size=32,
num_loader_workers=8,
num_eval_loader_workers=8,
use_noise_augment=True,
run_eval=True,
test_delay_epochs=-1,
epochs=1000,
print_step=50,
print_eval=True,
output_path=output_path,
add_blank=True,
datasets=[dataset_config],
# characters=characters,
enable_eos_bos_chars=True,
mixed_precision=False,
save_step=10000,
save_n_checkpoints=2,
save_best_after=5000,
text_cleaner="no_cleaners",
audio=audio_config,
test_sentences=[],
use_phonemes=True,
phoneme_language="be",
)
if __name__ == "__main__":
# INITIALIZE THE AUDIO PROCESSOR
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor.init_from_config(config)
# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# If characters are not defined in the config, default characters are passed to the config
tokenizer, config = TTSTokenizer.init_from_config(config)
# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(
dataset_config,
eval_split=True,
eval_split_max_size=config.eval_split_max_size,
eval_split_size=config.eval_split_size,
)
# INITIALIZE THE MODEL
# Models take a config object and a speaker manager as input
# Config defines the details of the model like the number of layers, the size of the embedding, etc.
# Speaker manager is used by multi-speaker models.
model = GlowTTS(config, ap, tokenizer, speaker_manager=None)
# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(
TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)
# AND... 3,2,1... 🚀
trainer.fit()
|